Agribiz embracing the rise of machines

A soyabean farmer in the Midwestern US wakes up before sunrise to prepare for large-scale planting of the crop. He grabs a coffee and powers up his laptop to farm.

May be he doesn’t even have a laptop, and he has a touch screen on one of his walls which he uses to adjust for irrigation levels, fertiliser application, verifying soil analysis and planting soyabeans. The farm has a sensor that can send him data on soil, moisture, soil aeration, etc.

All his farming machines are awaiting his instructions to change any inputs he may wish to make from his home.

The one thing he might have to still count on is good weather for planting, which, of course, he can monitor from anywhere he is.

This means he has more leisure and can even login from his vacation home in Florida to get in touch with his farm in Illinois.

A larger commercial firm which runs soyabean crushing plants globally will be looking to source soyabeans and sell soyameal to chicken producers and soya oil to French fries manufacturers.

They will be getting the data from the cloud, make predictive decisions on buying beans and selling meal and oil. Crush margins will be optimised efficiently. Bids to farmers and offers for meal and oil will go to their respective computers. This flow of information will help in real time decision making for trucking or barging.

Sensors will beam data on river drafts, barge traffic, load times, etc., leading to predictive decision analytics. Goods will reach sea ports where data on the contract of sale will be already uploaded in the machines.

The cranes should be able to load soyameal of correct quality and quantity without any human intervention. Data on goods loaded will be beamed to the cloud and can be picked up by an end-consumer in Thailand.

Based on the exact quality and arrival time, the customer can start making predictive decisions on different feed mixes for the business.

Financing, sales, and inventories will be bettered predicted and optimised. This is what we would call Internet of things as applied to agri-business.

I will leave myriad additional applications to the imagination of the reader. The point is that agribusiness will become more and more facilitated and influenced by machines – machines that enable human beings working in the sector to focus their judgment, time and resources on more and higher order decisions.

Fewer real-time, human decisions in areas where machine learning can incorporate prior data and outcomes mean reduced volatility due to idiosyncrasy and error.

The entire value chain’s efficiency will be dependent on the symbiotic working relationship between men and machines.

What does this all mean? First, decisions will be standardised. This will lead to very little variability within a single time horizon.

There will be inter-generational variability but given the predictive power of computing, this variability will be more an expected change than a sudden shock.

Second, cost structure will go down substantially. Standardisation will bring about economies of scale, and improve total factor productivity.

Third, there will be a great boom in information security and labour demand will dwindle.

Fourth, the competitive advantage will move towards firms having smart machines, great data, and phenomenal analytics. Lastly, firms will need to retrain or hire people who can adapt to the new environment of machines and cloud.

Today, only a fraction of agribusiness firms globally are incorporating this new reality into their value chain strategies. They will inevitably be joined by many more, as competitive dynamics necessitate a technological revolution in the sector.

Firms at the leading edge of this transformation are making long-term investments that are focused on the highest impact areas of real performance improvement. Instead of fearing the rise of the machines, they are embracing the opportunities presented.

The writer is based in London and is the founder and Managing Director of OpalCrest.